用卫星雷达图预测鄂毕湾海冰，还能评估预测可信度。
Data-Driven Uncertainty-Aware Forecasting of Sea Ice Conditions in the Gulf of Ob Based on Satellite Radar Imagery
- 结合哨兵1号影像与气象数据，用视频预测模型做海冰预报。
- 引入置信度混合机制，提升预测准确率和稳定性。
- 适合关注北极航行安全的科研与航运人员使用。
由于北极快速变暖和海冰显著减少，北极海洋活动增加，迫切需要高可靠的短期海冰预报以保障航行安全与运营效率。本文提出一种新型数据驱动方法，用于鄂毕湾海冰状况预测，利用哨兵-1卫星雷达图像序列、气象观测数据及GLORYS预报数据。方法融合了原本用于视觉任务的先进视频预测模型，并针对北极海冰动态特性设计了领域专用的数据预处理与增强技术。核心在于引入不确定性量化机制，评估预测可靠性，支持安全关键场景下的稳健决策。此外，提出基于置信度的模型混合机制，显著提升预报精度与模型鲁棒性，适用于多变的北极环境。实验结果表明，该方法在多个指标上优于基线模型，凸显不确定性量化与专用数据处理对实现安全高效作业与可靠预报的重要性。
原文摘要 · Abstract (English)
The increase in Arctic marine activity due to rapid warming and significant sea ice loss necessitates highly reliable, short-term sea ice forecasts to ensure maritime safety and operational efficiency. In this work, we present a novel data-driven approach for sea ice condition forecasting in the Gulf of Ob, leveraging sequences of radar images from Sentinel-1, weather observations, and GLORYS forecasts. Our approach integrates advanced video prediction models, originally developed for vision tasks, with domain-specific data preprocessing and augmentation techniques tailored to the unique challenges of Arctic sea ice dynamics. Central to our methodology is the use of uncertainty quantification to assess the reliability of predictions, ensuring robust decision-making in safety-critical applications. Furthermore, we propose a confidence-based model mixture mechanism that enhances forecast accuracy and model robustness, crucial for reliable operations in volatile Arctic environments. Our results demonstrate substantial improvements over baseline approaches, underscoring the importance of uncertainty quantification and specialized data handling for effective and safe operations and reliable forecasting.
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